ArticleAnnals of medicine2025
Development and validation of MRI-based radiomics model for clinical symptom stratification of extrinsic adenomyosis.
Article in Annals of medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Re: Development and validation of MRI-based radiomics model for clinical symptom stratification of extrinsic adenomyosis.Annals of medicine · 2026Article
- Regarding: "development and validation of MRI-based radiomics model for clinical symptom stratification of extrinsic adenomyosis".Annals of medicine · 2026Article
- The value of different machine learning radiomics based on DCE-MRI in predicting axillary lymph node status of breast cancer.Translational cancer research · 2025Article
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8 authors.
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Abstract
backgroundExtrinsic adenomyosis exhibits heterogeneous clinical symptoms, with pain being more commonly reported. The relationship between magnetic resonance imaging (MRI) feature and symptom remains unclear.
objectiveTo evaluate the performance of MRI radiomics model for differentiating symptom heterogeneity of extrinsic adenomyosis, pain, abnormal uterine bleeding (AUB), infertility, and no symptom. MATERIALS AND
methodsThis retrospective analysis included 405 patients with MRI-diagnosed extrinsic adenomyosis (January 2020-July 2022), randomly split 7:3 into training and test cohorts. Radiomic features were extracted from MRI-T2 image. Random forest algorithm was used to select the key radiomics features of different symptoms and develop the radiomic model by support vector machine algorithm. Multivariable logistic regression assessed clinical characteristics. A combined radiomics-clinical nomogram was created for symptom stratification.
resultsIn total 405 patients presented with 496 clinical symptoms. In the training and test cohorts, radiomics models achieved areas under the curve (AUCs) of 0.73/0.72 (pain), 0.82/0.76 (AUB), 0.84/0.80 (infertility), and 0.80/0.71 (no symptom). The multi-signature model (radiomic + clinical features) showed improved performance, with the nomogram demonstrating good stratification ability: AUCs of 0.78/0.78 (pain), 0.87/0.85 (AUB), 0.89/0.88 (infertility), and 0.84/0.81 (no symptom) in the training/test cohort.
conclusionWe identified the correlation between key radiomic features and clinical symptom of extrinsic adenomyosis. The machine learning-based MRI radiomics models have potential for symptom stratification of extrinsic adenomyosis and may potentially reduce unnecessary treatment.
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